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Multi-objective community detection based on memetic algorithm.

Peng Wu1, Li Pan1

  • 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China; National Engineering Laboratory for Information Content Analysis Technology, Shanghai Jiao Tong University, Shanghai, China.

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Summary
This summary is machine-generated.

This study introduces a multi-objective memetic algorithm to improve community detection in networks. The novel approach enhances the identification of multiple significant community structures, offering better network analysis.

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Area of Science:

  • Network science
  • Computational intelligence
  • Data mining

Background:

  • Community detection is crucial for understanding network structures and functions.
  • Existing single-objective methods struggle with identifying multiple significant community structures.
  • Population-based evolutionary algorithms offer global search but lack efficient local optimization.

Purpose of the Study:

  • To propose an effective multi-objective memetic algorithm for community detection.
  • To enhance the identification of multiple significant community structures in networks.
  • To improve the efficiency and stability of community detection algorithms.

Main Methods:

  • Combining a multi-objective evolutionary algorithm with a specialized local search procedure.
  • Developing a pseudonormal vector to integrate objective functions for a unified fitness evaluation.
  • Employing a network-specific local search strategy based on label propagation for efficient local optima discovery.

Main Results:

  • The local search procedure accelerates convergence to superior network partitions and increases algorithmic stability.
  • The proposed method demonstrates effectiveness in finding single partitions, outperforming classic community detection techniques.
  • The algorithm successfully identifies hierarchical network structures, enabling multi-resolution network analysis.

Conclusions:

  • The multi-objective memetic algorithm offers a robust and efficient solution for complex community detection tasks.
  • The integration of local search significantly improves the performance and stability of evolutionary community detection.
  • This method provides valuable insights into network organization across multiple scales.